The consolidation of open-source computer-assisted chemical synthesis data into a comprehensive database

Area of Science:

• Computational Chemistry
• Cheminformatics
• Data Science

Background:

• Computer-assisted chemical synthesis (CaCS) has seen renewed interest, driven by machine learning.
• Existing CaCS data is often limited in quantity, quality, and accessibility, hindering progress.
• The complexity of chemical synthesis has historically challenged computational approaches.

Purpose of the Study:

• To address data limitations in CaCS by creating a consolidated, open-source database.
• To provide a practical overview of the current state of CaCS data.
• To facilitate easier integration and utilization of CaCS data in research projects.

Main Methods:

• Consolidation of all relevant open-source CaCS data into a central repository.
• Development of a programmatic interface for data retrieval and management.
• Archiving original data for reusability and traceability, alongside efficiently stored processed data.

Main Results:

• The creation of the CaCS database, a central repository for CaCS data.
• The database offers programmatic access for tasks like synthesis prediction and retrosynthesis.
• A case study demonstrated the practical utilization and highlighted the database's advantages and disadvantages.

Conclusions:

• The CaCS database provides a systematic overview of open data sources in computer-assisted chemical synthesis.
• It offers a reproducible environment for transparent data processing and benchmarking.
• The database enhances data accessibility, reusability, and traceability for CaCS research.